import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


SCREAMING_SNAKE_CASE__		:       Optional[int]       						=       "\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

SCREAMING_SNAKE_CASE__		:       Tuple       						=       "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,    _KWARGS_DESCRIPTION    )
class    snake_case       (   datasets.Metric    ):




  def 						__lowercase(   self  :						int					)->							Tuple:




       """simple docstring"""
       return datasets.MetricInfo(
           description=_DESCRIPTION  ,			citation=_CITATION  ,			homepage='https://github.com/krishnap25/mauve'  ,			inputs_description=_KWARGS_DESCRIPTION  ,			features=datasets.Features(
               {
                   'predictions': datasets.Value('string'  ,			id='sequence'					),
                   'references': datasets.Value('string'  ,			id='sequence'					),
               }					)  ,			codebase_urls=['https://github.com/krishnap25/mauve']  ,			reference_urls=[
               'https://arxiv.org/abs/2102.01454',
               'https://github.com/krishnap25/mauve',
           ]  ,			)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						int  ,			a_  :						Union[str, Any]  ,			a_  :						Optional[int]=None  ,			a_  :						Union[str, Any]=None  ,			a_  :						Tuple=None  ,			a_  :						int=None  ,			a_  :						Any="auto"  ,			a_  :						List[Any]=-1  ,			a_  :						str=0.9  ,			a_  :						str=5  ,			a_  :						List[Any]=500  ,			a_  :						Dict="gpt2-large"  ,			a_  :						Tuple=-1  ,			a_  :						List[Any]=1024  ,			a_  :						Dict=25  ,			a_  :						Optional[int]=5  ,			a_  :						Any=True  ,			a_  :						List[str]=25  ,			)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		compute_mauve(
           p_text=a_  ,			q_text=a_  ,			p_features=a_  ,			q_features=a_  ,			p_tokens=a_  ,			q_tokens=a_  ,			num_buckets=a_  ,			pca_max_data=a_  ,			kmeans_explained_var=a_  ,			kmeans_num_redo=a_  ,			kmeans_max_iter=a_  ,			featurize_model_name=a_  ,			device_id=a_  ,			max_text_length=a_  ,			divergence_curve_discretization_size=a_  ,			mauve_scaling_factor=a_  ,			verbose=a_  ,			seed=a_  ,			)
       return out





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

